In [6]:
# Specify the directory containing the text files
df_folder = "./sp500_tickers_A-D_1min_1pppix"
# Get a list of text files in the directory and limit to the first 4 files
txt_files = [f for f in os.listdir(df_folder) if f.endswith(".txt")]
txt_files = txt_files[:4] # Limit to the first 4 files
# Print the list of selected text files
print(txt_files)
# Initialize an empty dictionary to store DataFrames
df = {}
# Loop through the selected text files and process each DataFrame
for txt_file in txt_files:
# Create the full path to the text file
txt_file_path = os.path.join(df_folder, txt_file)
try:
# Extract the symbol from the text file name
symbol = os.path.splitext(txt_file)[0]
# Read the text file into a DataFrame with column names
df[txt_file] = pd.read_csv(txt_file_path, names=["DateTime", "Open", "High", "Low", "Close", "Volume"])
# Convert the "DateTime" column to datetime format
df[txt_file]['DateTime'] = pd.to_datetime(df[txt_file]['DateTime'])
# Extract the date and time components from the "DateTime" column
df[txt_file]['Date'] = df[txt_file]['DateTime'].dt.date
df[txt_file]['Time'] = df[txt_file]['DateTime'].dt.time
# Convert the "Close" column to numeric, handling errors by coercing to NaN
df[txt_file]['Close'] = pd.to_numeric(df[txt_file]['Close'], errors='coerce')
# Specify a specific date for plotting
selected_date = pd.to_datetime('2016-10-18').date()
# Call functions to plot minute-by-minute and day-by-day data
plot_minute_by_minute(df[txt_file], selected_date, txt_file)
plot_day_by_day(df[txt_file], txt_file)
except Exception as e:
# Handle exceptions and print an error message
print(f"Error processing {txt_file_path}: {e}")
['AAL_1min.txt', 'AAPL_1min.txt', 'AAP_1min.txt', 'AA_1min.txt']
